✨ Improve standardization

Author: EssamWisam

docs/src/tutorials/standardization/notebook.jl

@@ -104,9 +104,9 @@ stand = Standardizer()  # This is our standardization transformer
 
 ## Create pipelines for each model variant
 logreg_pipe = logreg()  # Plain logistic regression
-logreg_std_pipe = Pipeline(stand, logreg())  # Logistic regression with standardization
+logreg_std_pipe = stand |> logreg()  # Logistic regression with standardization
 svm_pipe = svm()  # Plain SVM
-svm_std_pipe = Pipeline(stand, svm())  # SVM with standardization
+svm_std_pipe = stand |> svm()  # SVM with standardization
 
 # ## Model Evaluation
 #
@@ -123,6 +123,10 @@ models = [
 
 # Now we'll loop through each model, train it, make predictions, and calculate accuracy.
 # This will help us compare how standardization affects each model's performance.
+#
+# Note: As an alternative to the explicit fit!/predict workflow below, we could use:
+# evaluate(model, X, y, resampling=[(train, test),], measure=accuracy)
+# This shortcut handles the training, prediction, and evaluation in one step.
 
 ## Train and evaluate each model
 results = DataFrame(model = String[], accuracy = Float64[])

docs/src/tutorials/standardization/notebook.md

@@ -64,7 +64,7 @@ first(df, 5)
 ````
 
 ```@raw html
-<div><div style = "float: left;"><span>5×8 DataFrame</span></div><div style = "clear: both;"></div></div><div class = "data-frame" style = "overflow-x: scroll;"><table class = "data-frame" style = "margin-bottom: 6px;"><thead><tr class = "header"><th class = "rowNumber" style = "font-weight: bold; text-align: right;">Row</th><th style = "text-align: left;">NPreg</th><th style = "text-align: left;">Glu</th><th style = "text-align: left;">BP</th><th style = "text-align: left;">Skin</th><th style = "text-align: left;">BMI</th><th style = "text-align: left;">Ped</th><th style = "text-align: left;">Age</th><th style = "text-align: left;">Type</th></tr><tr class = "subheader headerLastRow"><th class = "rowNumber" style = "font-weight: bold; text-align: right;"></th><th title = "Int32" style = "text-align: left;">Int32</th><th title = "Float64" style = "text-align: left;">Float64</th><th title = "Int32" style = "text-align: left;">Int32</th><th title = "Int32" style = "text-align: left;">Int32</th><th title = "Float64" style = "text-align: left;">Float64</th><th title = "Float64" style = "text-align: left;">Float64</th><th title = "Int32" style = "text-align: left;">Int32</th><th title = "CategoricalArrays.CategoricalValue{String, UInt8}" style = "text-align: left;">Cat…</th></tr></thead><tbody><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">1</td><td style = "text-align: right;">5</td><td style = "text-align: right;">10086.0</td><td style = "text-align: right;">68</td><td style = "text-align: right;">28</td><td style = "text-align: right;">30.2</td><td style = "text-align: right;">0.364</td><td style = "text-align: right;">24</td><td style = "text-align: left;">No</td></tr><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">2</td><td style = "text-align: right;">7</td><td style = "text-align: right;">10195.0</td><td style = "text-align: right;">70</td><td style = "text-align: right;">33</td><td style = "text-align: right;">25.1</td><td style = "text-align: right;">0.163</td><td style = "text-align: right;">55</td><td style = "text-align: left;">Yes</td></tr><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">3</td><td style = "text-align: right;">5</td><td style = "text-align: right;">10077.0</td><td style = "text-align: right;">82</td><td style = "text-align: right;">41</td><td style = "text-align: right;">35.8</td><td style = "text-align: right;">0.156</td><td style = "text-align: right;">35</td><td style = "text-align: left;">No</td></tr><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">4</td><td style = "text-align: right;">0</td><td style = "text-align: right;">10165.0</td><td style = "text-align: right;">76</td><td style = "text-align: right;">43</td><td style = "text-align: right;">47.9</td><td style = "text-align: right;">0.259</td><td style = "text-align: right;">26</td><td style = "text-align: left;">No</td></tr><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">5</td><td style = "text-align: right;">0</td><td style = "text-align: right;">10107.0</td><td style = "text-align: right;">60</td><td style = "text-align: right;">25</td><td style = "text-align: right;">26.4</td><td style = "text-align: right;">0.133</td><td style = "text-align: right;">23</td><td style = "text-align: left;">No</td></tr></tbody></table></div>
+<div><div style = "float: left;"><span>5×8 DataFrame</span></div><div style = "clear: both;"></div></div><div class = "data-frame" style = "overflow-x: scroll;"><table class = "data-frame" style = "margin-bottom: 6px;"><thead><tr class = "header"><th class = "rowNumber" style = "font-weight: bold; text-align: right;">Row</th><th style = "text-align: left;">NPreg</th><th style = "text-align: left;">Glu</th><th style = "text-align: left;">BP</th><th style = "text-align: left;">Skin</th><th style = "text-align: left;">BMI</th><th style = "text-align: left;">Ped</th><th style = "text-align: left;">Age</th><th style = "text-align: left;">Type</th></tr><tr class = "subheader headerLastRow"><th class = "rowNumber" style = "font-weight: bold; text-align: right;"></th><th title = "Int32" style = "text-align: left;">Int32</th><th title = "Float64" style = "text-align: left;">Float64</th><th title = "Int32" style = "text-align: left;">Int32</th><th title = "Int32" style = "text-align: left;">Int32</th><th title = "Float64" style = "text-align: left;">Float64</th><th title = "Float64" style = "text-align: left;">Float64</th><th title = "Int32" style = "text-align: left;">Int32</th><th title = "CategoricalValue{String, UInt8}" style = "text-align: left;">Cat…</th></tr></thead><tbody><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">1</td><td style = "text-align: right;">5</td><td style = "text-align: right;">10086.0</td><td style = "text-align: right;">68</td><td style = "text-align: right;">28</td><td style = "text-align: right;">30.2</td><td style = "text-align: right;">0.364</td><td style = "text-align: right;">24</td><td style = "text-align: left;">No</td></tr><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">2</td><td style = "text-align: right;">7</td><td style = "text-align: right;">10195.0</td><td style = "text-align: right;">70</td><td style = "text-align: right;">33</td><td style = "text-align: right;">25.1</td><td style = "text-align: right;">0.163</td><td style = "text-align: right;">55</td><td style = "text-align: left;">Yes</td></tr><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">3</td><td style = "text-align: right;">5</td><td style = "text-align: right;">10077.0</td><td style = "text-align: right;">82</td><td style = "text-align: right;">41</td><td style = "text-align: right;">35.8</td><td style = "text-align: right;">0.156</td><td style = "text-align: right;">35</td><td style = "text-align: left;">No</td></tr><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">4</td><td style = "text-align: right;">0</td><td style = "text-align: right;">10165.0</td><td style = "text-align: right;">76</td><td style = "text-align: right;">43</td><td style = "text-align: right;">47.9</td><td style = "text-align: right;">0.259</td><td style = "text-align: right;">26</td><td style = "text-align: left;">No</td></tr><tr><td class = "rowNumber" style = "font-weight: bold; text-align: right;">5</td><td style = "text-align: right;">0</td><td style = "text-align: right;">10107.0</td><td style = "text-align: right;">60</td><td style = "text-align: right;">25</td><td style = "text-align: right;">26.4</td><td style = "text-align: right;">0.133</td><td style = "text-align: right;">23</td><td style = "text-align: left;">No</td></tr></tbody></table></div>
 ```
 
 ### Data Type Conversion
@@ -151,9 +151,9 @@ stand = Standardizer()  # This is our standardization transformer
 
 # Create pipelines for each model variant
 logreg_pipe = logreg()  # Plain logistic regression
-logreg_std_pipe = Pipeline(stand, logreg())  # Logistic regression with standardization
+logreg_std_pipe = stand |> logreg()  # Logistic regression with standardization
 svm_pipe = svm()  # Plain SVM
-svm_std_pipe = Pipeline(stand, svm())  # SVM with standardization
+svm_std_pipe = stand |> svm()  # SVM with standardization
 ````
 
 ````
@@ -191,7 +191,7 @@ models = [
 ````
 
 ````
-4-element Vector{Tuple{String, MLJModelInterface.Supervised}}:
+4-element Vector{Tuple{String, Supervised}}:
  ("Logistic Regression", LogisticClassifier(lambda = 2.220446049250313e-16, …))
  ("Logistic Regression (standardized)", ProbabilisticPipeline(standardizer = Standardizer(features = Symbol[], …), …))
  ("SVM", SVC(kernel = RadialBasis, …))
@@ -201,6 +201,10 @@ models = [
 Now we'll loop through each model, train it, make predictions, and calculate accuracy.
 This will help us compare how standardization affects each model's performance.
 
+Note: As an alternative to the explicit fit!/predict workflow below, we could use:
+evaluate(model, X, y, resampling=[(train, test),], measure=accuracy)
+This shortcut handles the training, prediction, and evaluation in one step.
+
 ````julia
 # Train and evaluate each model
 results = DataFrame(model = String[], accuracy = Float64[])
@@ -243,10 +247,10 @@ end
 │ 
 │ In the present case:
 │ 
-│ scitype(data) = Tuple{ScientificTypesBase.Table{Union{AbstractVector{ScientificTypesBase.Continuous}, AbstractVector{ScientificTypesBase.Count}}}, AbstractVector{ScientificTypesBase.Multiclass{2}}}
+│ scitype(data) = Tuple{Table{Union{AbstractVector{Continuous}, AbstractVector{Count}}}, AbstractVector{Multiclass{2}}}
 │ 
-│ fit_data_scitype(model) = Tuple{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, AbstractVector{<:ScientificTypesBase.Finite}}
-└ @ MLJBase ~/.julia/packages/MLJBase/F1Eh6/src/machines.jl:237
+│ fit_data_scitype(model) = Tuple{Table{<:AbstractVector{<:Continuous}}, AbstractVector{<:Finite}}
+└ @ MLJBase ~/.julia/packages/MLJBase/7nGJF/src/machines.jl:237
 [ Info: Training machine(LogisticClassifier(lambda = 2.220446049250313e-16, …), …).
 ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}}
 │   optim_options: Optim.Options{Float64, Nothing}
@@ -273,10 +277,10 @@ end
 │ 
 │ In the present case:
 │ 
-│ scitype(data) = Tuple{ScientificTypesBase.Table{Union{AbstractVector{ScientificTypesBase.Continuous}, AbstractVector{ScientificTypesBase.Count}}}, AbstractVector{ScientificTypesBase.Multiclass{2}}}
+│ scitype(data) = Tuple{Table{Union{AbstractVector{Continuous}, AbstractVector{Count}}}, AbstractVector{Multiclass{2}}}
 │ 
-│ fit_data_scitype(model) = Union{Tuple{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, AbstractVector{<:ScientificTypesBase.Finite}}, Tuple{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, AbstractVector{<:ScientificTypesBase.Finite}, Any}}
-└ @ MLJBase ~/.julia/packages/MLJBase/F1Eh6/src/machines.jl:237
+│ fit_data_scitype(model) = Union{Tuple{Table{<:AbstractVector{<:Continuous}}, AbstractVector{<:Finite}}, Tuple{Table{<:AbstractVector{<:Continuous}}, AbstractVector{<:Finite}, Any}}
+└ @ MLJBase ~/.julia/packages/MLJBase/7nGJF/src/machines.jl:237
 [ Info: Training machine(SVC(kernel = RadialBasis, …), …).
 [ Info: Training machine(DeterministicPipeline(standardizer = Standardizer(features = Symbol[], …), …), …).
 [ Info: Training machine(:standardizer, …).